J Pharm Biomed Anal. 2023 Oct 5;237:115770. doi: 10.1016/j.jpba.2023.115770. Online ahead of print.

ABSTRACT

Soon after the outset of the Coronavirus Disease 2019 (COVID-19) pandemic (March-April 2020), formulations of the old antimalarial racemic drug hydroxychloroquine (HCQ) sulfate were authorized by the U.S. Food and Drug Administration (FDA) for emergency treatment of hospitalized patients with COVID-19. A call for the chiral switch of HCQ to the single enantiomer (S)-(+)-HCQ for treating the disease followed. The above authorizations were later withdrawn. Angiotensin-converting enzyme 2 (ACE2) has been recognized to be the forward receptor of SARS-CoV-2, the virus responsible for COVID-19. The objective of the present study was to evaluate the chiral distinction in the potential preferential binding of the HCQ enantiomers to ACE2, as a basis for its future drug repurposing, using high-field solution Nuclear Magnetic Resonance (NMR) spectroscopy. Proton selective spin-lattice relaxation rates were measured for selected diagnostic nuclei; in particular, protons belonging to the quinoline ring proved to be the most affected by the presence of the protein, for both (S)-(+)-HCQ and (R)-(-)-HCQ enantiomers. An increase in mono-selective relaxation rates was observed for both enantiomers. A significant difference in the magnitude of the increase was detected for all protons investigated, up to a 5-fold and an 8-fold increase in the case of (R)-(-)-HCQ and (S)-(+)-HCQ, respectively. Furthermore, comparison between the normalized mono-selective relaxation rates of the two HCQ enantiomers in their binary mixtures with ACE2 pointed out a certain preference for the (S)-(+)-HCQ enantiomer over (R)-(-)-HCQ in the interaction with ACE2. The findings form the basis for a future application of the drug repurposing/chiral-switch combination strategy to racemic HCQ in previously reported indications for hydroxychloroquine treatment and in the search for new indications in which ACE2 receptors are involved.

PMID:37879140 | DOI:10.1016/j.jpba.2023.115770

J Biomol Struct Dyn. 2023 Oct 25:1-17. doi: 10.1080/07391102.2023.2272753. Online ahead of print.

ABSTRACT

In silico docking studies serve as a swift and efficient means to sift through a vast array of natural and synthetic small molecules, aiding in the identification of potential inhibitors for cancer biomarkers. One such biomarker, ceruloplasmin (CP), has been implicated in various tumor types due to its overexpression, earning it recognition as a marker of aggressive tumors. This study focused on pinpointing inhibitors for the CP -Myeloperoxidase (MPO) interaction site, a complex formation known to impede HOCl production, a crucial process for inducing apoptotic cell death in tumor cells. The initial phase of our investigation involved in silico docking studies, which screened a diverse library of phytochemicals and marine compounds. Through this process, we identified several promising drug candidates based on their binding affinities. Subsequently, these candidates underwent rigorous filtration based on Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties. Finally, we subjected the selected compounds to molecular dynamics (MDs) simulation to further assess their viability. Lycoperoside F, a steroidal alkaloid glycoside derived from tomatoes (Lycopersicon esculentum), stood out with notable interactions at the binding site. Another noteworthy compound was Xyloglucan (XG) oligosaccharides, predominantly found in the primary cell walls of higher plants. During the subsequent MDs simulations, these interactions were accompanied by highly stable root mean square deviation (RMSD) plots, signifying the consistency and robustness of the observed MDs behavior. XG oligosaccharides demonstrated the highest binding affinity with CP, reaffirming their potential as strong candidates. Additionally, Ardimerin digallate, known as a retroviral ribonuclease H inhibitor for HIV-1 and HIV-2, displayed favorable interactions at the MPO interaction site. Given that promising drug candidates must meet stringent criteria, including non-toxicity, effectiveness, specificity, stability and potency, these phytochemicals have the potential to progress to in vitro studies as CP inhibitors. Ultimately, this could contribute to the suppression of tumor growth, marking a significant step in cancer treatment research.Communicated by Ramaswamy H. Sarma.

PMID:37878121 | DOI:10.1080/07391102.2023.2272753

Nat Rev Drug Discov. 2023 Oct 23. doi: 10.1038/d41573-023-00168-9. Online ahead of print.

NO ABSTRACT

PMID:37872324 | DOI:10.1038/d41573-023-00168-9

ChemMedChem. 2023 Oct 23:e202300282. doi: 10.1002/cmdc.202300282. Online ahead of print.

ABSTRACT

Drug repurposing has emerged as an attractive strategy for accelerating drug discovery for cancer treatment. In this study, we investigated combining Tranylcypromine (TCP) with a number of well-characterized drugs. Among these combinations, ML385 exhibited synergistic effects in combination with TCP. Specifically, our results showed that the combination of TCP and ML385 resulted in a significant reduction in tumor proliferation while neither drug affected cancer cell growth meaningfully on its own. While further studies are needed to understand fully the extent of the synergistic efficacy, the underlying respective mechanisms and the potential side effects of this approach, our study has yielded a promising start for the development of an effective combination cancer therapy.

PMID:37871186 | DOI:10.1002/cmdc.202300282

Naunyn Schmiedebergs Arch Pharmacol. 2023 Oct 23. doi: 10.1007/s00210-023-02796-9. Online ahead of print.

ABSTRACT

To gain a comprehensive overview of the landscape of clinical trials for the H1-receptor antagonists (H1R antagonists) cetirizine, levocetirizine, loratadine, desloratadine, and fexofenadine and their potential use cases in drug repurposing (the use of well-known drugs outside the scope of the original medical indication), we analyzed trials from clincialtrials.gov using novel custom-coded software, which itself is also a key emphasis of this paper. To automate data acquisition from clincialtrials.gov via its API, data processing, and storage, we created custom software by leveraging a variety of open-source tools. Data were stored in a relational database and annotated facilitating a specially adapted web application. Through the data analysis, we identified use cases for repurposing and reviewed backgrounds and results in the scientific literature. Even though we found very few trials with published results for repurpose indications, extended literature research revealed some prominent use cases: Cetirizine seems promising in mitigating infusion-associated reactions and is also more effective than placebo in the treatment of androgenetic alopecia. Loratadine may be beneficial in the prophylaxis of G-CSF-related bone pain. In COVID-19, H1R antagonists may be helpful, but placebo-controlled scientific evidence is needed. For asthma, the effect of H1R antagonists only seems to be secondary by alleviating allergy symptoms. Our novel method to find potential use cases for repurposing of H1R antagonists allows for high automation, reduces human error, and was successful in revealing potential areas of interest. The software could be used for similar research questions and analyses in the future.

PMID:37870580 | DOI:10.1007/s00210-023-02796-9

Heliyon. 2023 Oct 11;9(10):e20889. doi: 10.1016/j.heliyon.2023.e20889. eCollection 2023 Oct.

ABSTRACT

Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder with a worldwide prevalence of 6-10 % of women of reproductive age. PCOS is a risk factor for cardiometabolic disorders such as type 2 diabetes, myocardial infarction, and stroke in addition to exhibiting signs of hyperandrogenism and anovulation. However, there is no known cure for PCOS, and medications have only ever been used symptomatically, with a variety of adverse effects. Drugs made from natural plant products may help treat PCOS because several plant extracts have been widely recognized to lessen the symptoms of PCOS. In light of this, 72 current studies on natural products with the potential to control PCOS were examined. By controlling the PI3K/AKT signaling pathway and decreasing NF-κB and cytokines such as tumor necrosis factor (TNF), interleukin-1 (IL-1), and interleukin-6 (IL-6), certain plant-derived chemicals might reduce inflammation. Other substances altered the HPO axis, which normalized hormones. Additionally, other plant components increased glutathione (GSH), superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) levels to reduce radiation-induced oxidative stress. The other substances prevented autophagy by impairing beclin 1, autophagy-related 5 (ATG5), and microtubule-associated protein 1A/1B-light chain 3 - II (LC3- II). The main focus of this comprehensive review is the possibility of plant extracts as natural bio-resources of PCOS treatment by regulating inflammation, hormones, reactive oxygen species (ROS), or autophagy.

PMID:37867816 | PMC:PMC10589870 | DOI:10.1016/j.heliyon.2023.e20889

Curr Diabetes Rev. 2023 Oct 20. doi: 10.2174/0115733998249061231009093006. Online ahead of print.

ABSTRACT

BACKGROUND: Patients with diabetes suffer from major complications like Diabetic Retinopathy, Diabetic Coronary Artery Disease, and Diabetic Foot ulcers (DFUs). Diabetes complications are a group of ailments whose recovery time is especially delayed, irrespective of the underlying reason. The longer duration of wound healing enhances the probability of problems like sepsis and amputation. The delayed healing makes it more critical for research focus. By understanding the molecular pathogenesis of diabetic wounds, it is quite easy to target the molecules involved in the healing of wounds. Recent research on beta-adrenergic blocking drugs has revealed that these classes of drugs possess therapeutic potential in the healing of DFUs. However, because the order of events in defective healing is adequately defined, it is possible to recognize moieties that are currently in the market that are recognized to aim at one or several identified molecular processes.

OBJECTIVE: The aim of this study was to explore some molecules with different therapeutic categories that have demonstrated favorable effects in improving diabetic wound healing, also called the repurposing of drugs.

METHOD: Various databases like PubMed/Medline, Google Scholar and Web of Science (WoS) of all English language articles were searched, and relevant information was collected regarding the role of beta-adrenergic blockers in diabetic wounds or diabetic foot ulcers (DFUs) using the relevant keywords for the literature review.

RESULT: The potential beta-blocking agents and their mechanism of action in diabetic foot ulcers were studied, and it was found that these drugs have a profound effect on diabetic foot ulcer healing as per reported literatures.

CONCLUSION: There is a need to move forward from preclinical studies to clinical studies to analyze clinical findings to determine the effectiveness and safety of some beta-antagonists in diabetic foot ulcer treatment.

PMID:37867269 | DOI:10.2174/0115733998249061231009093006

Comput Biol Chem. 2023 Oct 11;107:107969. doi: 10.1016/j.compbiolchem.2023.107969. Online ahead of print.

ABSTRACT

Protein-ligand interaction plays a crucial role in drug discovery, facilitating efficient drug development and enabling drug repurposing. Several computational algorithms, such as Graph Neural Networks and Convolutional Neural Networks, have been proposed to predict the binding affinity using the three-dimensional structure of ligands and proteins. However, there are limitations due to the need for experimental characterization of the three-dimensional structure of protein sequences, which is still lacking for some proteins. Moreover, these models often suffer from unnecessary complexity, resulting in extraneous computations. This study presents ResBiGAAT, a novel deep learning model that combines a deep Residual Bidirectional Gated Recurrent Unit with two-sided self-attention mechanisms. ResBiGAAT leverages protein and ligand sequence-level features and their physicochemical properties to efficiently predict protein-ligand binding affinity. Through rigorous evaluation using 5-fold cross-validation, we demonstrate the performance of our proposed approach. The model exhibits competitive performance on an external dataset, highlighting its generalizability. Our publicly available web interface, located at resbigaat.streamlit.app, allows users to conveniently input protein and ligand sequences to estimate binding affinity.

PMID:37866117 | DOI:10.1016/j.compbiolchem.2023.107969

Sci Rep. 2023 Oct 21;13(1):18022. doi: 10.1038/s41598-023-45347-1.

ABSTRACT

Drug designing is high-priced and time taking process with low success rate. To overcome this obligation, computational drug repositioning technique is being promptly used to predict the possible therapeutic effects of FDA approved drugs against multiple diseases. In this computational study, protein modeling, shape-based screening, molecular docking, pharmacogenomics, and molecular dynamic simulation approaches have been utilized to retrieve the FDA approved drugs against AD. The predicted MADD protein structure was designed by homology modeling and characterized through different computational resources. Donepezil and galantamine were implanted as standard drugs and drugs were screened out based on structural similarities. Furthermore, these drugs were evaluated and based on binding energy (Kcal/mol) profiles against MADD through PyRx tool. Moreover, pharmacogenomics analysis showed good possible associations with AD mediated genes and confirmed through detail literature survey. The best 6 drug (darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar) further docked and analyzed their interaction behavior through hydrogen binding. Finally, MD simulation study were carried out on these drugs and evaluated their stability behavior by generating root mean square deviation and fluctuations (RMSD/F), radius of gyration (Rg) and soluble accessible surface area (SASA) graphs. Taken together, darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar displayed good lead like profile as compared with standard and can be used as possible therapeutic agent in the treatment of AD after in-vitro and in-vivo assessment.

PMID:37865690 | DOI:10.1038/s41598-023-45347-1

Drug Resist Updat. 2023 Oct 10;71:101011. doi: 10.1016/j.drup.2023.101011. Online ahead of print.

ABSTRACT

The overexpression of the human ATP-binding cassette (ABC) transporters in cancer cells is a common mechanism involved in developing multidrug resistance (MDR). Unfortunately, there are currently no approved drugs specifically designed to treat multidrug-resistant cancers, making MDR a significant obstacle to successful chemotherapy. Despite over two decades of research, developing transporter-specific inhibitors for clinical use has proven to be a challenging endeavor. As an alternative approach, drug repurposing has gained traction as a more practical method to discover clinically effective modulators of drug transporters. This involves exploring new indications for already-approved drugs, bypassing the lengthy process of developing novel synthetic inhibitors. In this context, we will discuss the mechanisms of ABC drug transporters ABCB1 and ABCG2, their roles in cancer MDR, and the inhibitors that have been evaluated for their potential to reverse MDR mediated by these drug transporters. Our focus will be on providing an up-to-date report on approved drugs tested for their inhibitory activities against these drug efflux pumps. Lastly, we will explore the challenges and prospects of repurposing already approved medications for clinical use to overcome chemoresistance in patients with high tumor expression of ABCB1 and/or ABCG2.

PMID:37865067 | DOI:10.1016/j.drup.2023.101011

Brief Bioinform. 2023 Sep 22;24(6):bbad371. doi: 10.1093/bib/bbad371.

ABSTRACT

Drug-gene interaction prediction occupies a crucial position in various areas of drug discovery, such as drug repurposing, lead discovery and off-target detection. Previous studies show good performance, but they are limited to exploring the binding interactions and ignoring the other interaction relationships. Graph neural networks have emerged as promising approaches owing to their powerful capability of modeling correlations under drug-gene bipartite graphs. Despite the widespread adoption of graph neural network-based methods, many of them experience performance degradation in situations where high-quality and sufficient training data are unavailable. Unfortunately, in practical drug discovery scenarios, interaction data are often sparse and noisy, which may lead to unsatisfactory results. To undertake the above challenges, we propose a novel Dynamic hyperGraph Contrastive Learning (DGCL) framework that exploits local and global relationships between drugs and genes. Specifically, graph convolutions are adopted to extract explicit local relations among drugs and genes. Meanwhile, the cooperation of dynamic hypergraph structure learning and hypergraph message passing enables the model to aggregate information in a global region. With flexible global-level messages, a self-augmented contrastive learning component is designed to constrain hypergraph structure learning and enhance the discrimination of drug/gene representations. Experiments conducted on three datasets show that DGCL is superior to eight state-of-the-art methods and notably gains a 7.6% performance improvement on the DGIdb dataset. Further analyses verify the robustness of DGCL for alleviating data sparsity and over-smoothing issues.

PMID:37864294 | DOI:10.1093/bib/bbad371

Eur J Pediatr. 2023 Oct 21. doi: 10.1007/s00431-023-05263-y. Online ahead of print.

ABSTRACT

Noonan syndrome belongs to the family of RASopathies, a group of multiple congenital anomaly disorders caused by pathogenic variants in genes encoding components or regulators of the RAS/mitogen-activated protein kinase (MAPK) signalling pathway. Collectively, all these pathogenic variants lead to increased RAS/MAPK activation. The better understanding of the molecular mechanisms underlying the different manifestations of NS and RASopathies has led to the identification of molecular targets for specific pharmacological interventions. Many specific agents (e.g. SHP2 and MEK inhibitors) have already been developed for the treatment of RAS/MAPK-driven malignancies. In addition, other molecules with the property of modulating RAS/MAPK activation are indicated in non-malignant diseases (e.g. C-type natriuretic peptide analogues in achondroplasia or statins in hypercholesterolemia). Conclusion: Drug repositioning of these molecules represents a challenging approach to treat or prevent medical complications associated with RASopathies. What is Known: • Noonan syndrome and related disorders are caused by pathogenic variants in genes encoding components or regulators of the RAS/mitogen-activated protein kinase (MAPK) signalling pathway, resulting in increased activation of this pathway. • This group of disorders is now known as RASopathies and represents one of the largest groups of multiple congenital anomaly diseases known. What is New: • The identification of pathophysiological mechanisms provides new insights into the development of specific therapeutic strategies, in particular treatment aimed at reducing RAS/MAPK hyperactivation. • Drug repositioning of specific agents already developed for the treatment of malignant (e.g. SHP2 and MEK inhibitors) or non-malignant diseases (e.g. C-type natriuretic peptide analogues in achondroplasia or statins in hypercholesterolaemia) represents a challenging approach to the treatment of RASopathies.

PMID:37863846 | DOI:10.1007/s00431-023-05263-y

Bioinformatics. 2023 Oct 20:btad644. doi: 10.1093/bioinformatics/btad644. Online ahead of print.

ABSTRACT

MOTIVATION: The reconstruction of small key regulatory networks that explain the differences in the development of cell (sub)types from single-cell RNA sequencing is a yet unresolved computational problem.

RESULTS: To this end, we have developed SCANet, an all-in-one package for single-cell profiling that covers the whole differential mechanotyping workflow, from inference of trait/cell-type-specific gene co-expression modules, driver gene detection, and transcriptional gene regulatory network reconstruction to mechanistic drug repurposing candidate prediction. To illustrate the power of SCANet, we examined data from two studies. First, we identify the drivers of the mechanotype of a cytokine storm associated with increased mortality in patients with acute respiratory illness. Secondly, we find 20 drugs for 8 potential pharmacological targets in cellular driver mechanisms in the intestinal stem cells of obese mice.

AVAILABILITY: SCANet is a free, open-source, and user-friendly Python package that can be seamlessly integrated into single-cell-based systems medicine research and mechanistic drug discovery.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:37862243 | DOI:10.1093/bioinformatics/btad644

J Med Internet Res. 2023 Oct 20;25:e45225. doi: 10.2196/45225.

ABSTRACT

BACKGROUND: The global pandemics of severe acute respiratory syndrome, Middle East respiratory syndrome, and COVID-19 have caused unprecedented crises for public health. Coronaviruses are constantly evolving, and it is unknown which new coronavirus will emerge and when the next coronavirus will sweep across the world. Knowledge graphs are expected to help discover the pathogenicity and transmission mechanism of viruses.

OBJECTIVE: The aim of this study was to discover potential targets and candidate drugs to repurpose for coronaviruses through a knowledge graph-based approach.

METHODS: We propose a computational and evidence-based knowledge discovery approach to identify potential targets and candidate drugs for coronaviruses from biomedical literature and well-known knowledge bases. To organize the semantic triples extracted automatically from biomedical literature, a semantic conversion model was designed. The literature knowledge was associated and integrated with existing drug and gene knowledge through semantic mapping, and the coronavirus knowledge graph (CovKG) was constructed. We adopted both the knowledge graph embedding model and the semantic reasoning mechanism to discover unrecorded mechanisms of drug action as well as potential targets and drug candidates. Furthermore, we have provided evidence-based support with a scoring and backtracking mechanism.

RESULTS: The constructed CovKG contains 17,369,620 triples, of which 641,195 were extracted from biomedical literature, covering 13,065 concept unique identifiers, 209 semantic types, and 97 semantic relations of the Unified Medical Language System. Through multi-source knowledge integration, 475 drugs and 262 targets were mapped to existing knowledge, and 41 new drug mechanisms of action were found by semantic reasoning, which were not recorded in the existing knowledge base. Among the knowledge graph embedding models, TransR outperformed others (mean reciprocal rank=0.2510, Hits@10=0.3505). A total of 33 potential targets and 18 drug candidates were identified for coronaviruses. Among them, 7 novel drugs (ie, quinine, nelfinavir, ivermectin, asunaprevir, tylophorine, Artemisia annua extract, and resveratrol) and 3 highly ranked targets (ie, angiotensin converting enzyme 2, transmembrane serine protease 2, and M protein) were further discussed.

CONCLUSIONS: We showed the effectiveness of a knowledge graph-based approach in potential target discovery and drug repurposing for coronaviruses. Our approach can be extended to other viruses or diseases for biomedical knowledge discovery and relevant applications.

PMID:37862061 | DOI:10.2196/45225

OMICS. 2023 Oct;27(10):483-493. doi: 10.1089/omi.2023.0127.

ABSTRACT

Chronic inflammation is an important contributor to tumorigenesis in many tissues. However, the underlying mechanisms of inflammatory signaling in the tumor microenvironment are not yet fully understood in various cancers. Therefore, this study aimed to uncover the gene expression signatures of inflammation-associated proteins that lead to tumorigenesis, and with an eye to discovery of potential system biomarkers and novel drug candidates in oncology. Gene expression profiles associated with 12 common cancers (e.g., breast invasive carcinoma, colon adenocarcinoma, liver hepatocellular carcinoma, and prostate adenocarcinoma) from The Cancer Genome Atlas were retrieved and mapped to inflammation-related gene sets. Subsequently, the inflammation-associated differentially expressed genes (i-DEGs) were determined. The i-DEGs common in all cancers were proposed as tumor inflammation signatures (TIS) after pan-cancer analysis. A TIS, consisting of 45 proteins, was evaluated as a potential system biomarker based on its prognostic forecasting and secretion profiles in multiple tissues. In addition, i-DEGs for each cancer type were used as queries for drug repurposing. Narciclasine, parthenolide, and homoharringtonine were identified as potential candidates for drug repurposing. Biomarker candidates in relation to inflammation were identified such as KNG1, SPP1, and MIF. Collectively, these findings inform precision diagnostics development to distinguish individual cancer types, and can also pave the way for novel prognostic decision tools and repurposed drugs across multiple cancers. These new findings and hypotheses warrant further research toward precision/personalized medicine in oncology. Pan-cancer analysis of inflammatory mediators can open up new avenues for innovation in cancer diagnostics and therapeutics.

PMID:37861711 | DOI:10.1089/omi.2023.0127

Front Med (Lausanne). 2023 Oct 4;10:1089087. doi: 10.3389/fmed.2023.1089087. eCollection 2023.

ABSTRACT

BACKGROUND: The gold standard for gathering data from electronic health records (EHR) has been manual data extraction; however, this requires vast resources and personnel. Automation of this process reduces resource burdens and expands research opportunities.

OBJECTIVE: This study aimed to determine the feasibility and reliability of automated data extraction in a large registry of adult COVID-19 patients.

MATERIALS AND METHODS: This observational study included data from sites participating in the SCCM Discovery VIRUS COVID-19 registry. Important demographic, comorbidity, and outcome variables were chosen for manual and automated extraction for the feasibility dataset. We quantified the degree of agreement with Cohen's kappa statistics for categorical variables. The sensitivity and specificity were also assessed. Correlations for continuous variables were assessed with Pearson's correlation coefficient and Bland-Altman plots. The strength of agreement was defined as almost perfect (0.81-1.00), substantial (0.61-0.80), and moderate (0.41-0.60) based on kappa statistics. Pearson correlations were classified as trivial (0.00-0.30), low (0.30-0.50), moderate (0.50-0.70), high (0.70-0.90), and extremely high (0.90-1.00).

MEASUREMENTS AND MAIN RESULTS: The cohort included 652 patients from 11 sites. The agreement between manual and automated extraction for categorical variables was almost perfect in 13 (72.2%) variables (Race, Ethnicity, Sex, Coronary Artery Disease, Hypertension, Congestive Heart Failure, Asthma, Diabetes Mellitus, ICU admission rate, IMV rate, HFNC rate, ICU and Hospital Discharge Status), and substantial in five (27.8%) (COPD, CKD, Dyslipidemia/Hyperlipidemia, NIMV, and ECMO rate). The correlations were extremely high in three (42.9%) variables (age, weight, and hospital LOS) and high in four (57.1%) of the continuous variables (Height, Days to ICU admission, ICU LOS, and IMV days). The average sensitivity and specificity for the categorical data were 90.7 and 96.9%.

CONCLUSION AND RELEVANCE: Our study confirms the feasibility and validity of an automated process to gather data from the EHR.

PMID:37859860 | PMC:PMC10583598 | DOI:10.3389/fmed.2023.1089087

J Cell Mol Med. 2023 Oct 19. doi: 10.1111/jcmm.18002. Online ahead of print.

ABSTRACT

Papillary thyroid carcinoma (PTC) is one of the most common endocrine carcinomas worldwide and the aetiology of this cancer is still not well understood. Therefore, it remains important to understand the disease mechanism and find prognostic biomarkers and/or drug candidates for PTC. Compared with approaches based on single-gene assessment, network medicine analysis offers great promise to address this need. Accordingly, in the present study, we performed differential co-expressed network analysis using five transcriptome datasets in patients with PTC and healthy controls. Following meta-analysis of the transcriptome datasets, we uncovered common differentially expressed genes (DEGs) for PTC and, using these genes as proxies, found a highly clustered differentially expressed co-expressed module: a 'PTC-module'. Using independent data, we demonstrated the high prognostic capacity of the PTC-module and designated this module as a prognostic systems biomarker. In addition, using the nodes of the PTC-module, we performed drug repurposing and text mining analyzes to identify novel drug candidates for the disease. We performed molecular docking simulations, and identified: 4-demethoxydaunorubicin hydrochloride, AS605240, BRD-A60245366, ER 27319 maleate, sinensetin, and TWS119 as novel drug candidates whose efficacy was also confirmed by in silico analyzes. Consequently, we have highlighted here the need for differential co-expression analysis to gain a systems-level understanding of a complex disease, and we provide candidate prognostic systems biomarker and novel drugs for PTC.

PMID:37859510 | DOI:10.1111/jcmm.18002

Curr Rev Clin Exp Pharmacol. 2023 Oct 18. doi: 10.2174/0127724328245156231008154045. Online ahead of print.

ABSTRACT

Inflammatory bowel disease (IBD), represented by Crohn's disease (CD) and ulcerative colitis (UC), is a chronic inflammatory disorder of the gastrointestinal tract (GIT) characterized by chronic relapsing intestinal inflammation, abdominal pain, cramping, loss of appetite, fatigue, diarrhoea, and weight loss. Although the etiology of IBD remains unclear, it is believed to be an interaction between genes, and environmental factors, such as an imbalance of the intestinal microbiota, changing food habits, an ultra-non-hygiene environment, and an inappropriate immune system. The development of novel effective therapies is stymied by a lack of understanding of the aetiology of IBD. The current therapy involves the use of aminosalicylates, immunosuppressants, and corticosteroids that can effectively manage symptoms, induce and sustain remission, prevent complications, modify the course of the disease, provide diverse treatment options, showcase advancements in biologic therapies, and enhance the overall quality of life. However, the efficacy of current therapy is overshadowed by a plethora of adverse effects, such as loss of weight, mood swings, skin issues, loss of bone density, higher vulnerability to infections, and elevated blood pressure. Biologicals, like anti-tumour necrosis factor agents, can stimulate an autoimmune response in certain individuals that may diminish the effectiveness of the medication over time, necessitating a switch to alternative treatments. The response of IBD patients to current drug therapy is quite varied, which can lead to disease flares that underlines the urgent need to explore alternative treatment option to address the unmet need of developing new treatment strategies for IBD with high efficacy and fewer adverse effects. Drug repurposing is a novel strategy where existing drugs that have already been validated safe in patients for the management of certain diseases are redeployed to treat other, unindicated diseases. The present narrative review focuses on potential drug candidates that could be repurposed for the management of IBD using on-target and off-target strategies. It covers their preclinical, clinical assessment, mechanism of action, and safety profiles, and forecasts their appropriateness in the management of IBD. The review presents useful insights into the most promising candidates for repurposing, like anti-inflammatory and anti-apoptotic troxerutin, which has been found to improve the DSS-induced colitis in rats, an antiosteoarthritic drug diacetylrhein that has been found to have remarkable ameliorating effects on DSS-induced colitis via anti-oxidant and anti-inflammatory properties and by influencing both apoptosis and pyroptosis. Topiramate, an antiepileptic and anticonvulsant drug, has remarkably decreased overall pathophysiological and histopathological events in the experimental model of IBD in rodents by its cytokine inhibitory action.

PMID:37859409 | DOI:10.2174/0127724328245156231008154045

Aging Cell. 2023 Oct 19:e13944. doi: 10.1111/acel.13944. Online ahead of print.

ABSTRACT

Drug repositioning strategy represents a valid tool to accelerate the pharmacological development through the identification of new applications for already existing compounds. In this view, we aimed at discovering molecules able to trigger telomere-localized DNA damage and tumor cell death. By applying an automated high-content spinning-disk microscopy, we performed a screening aimed at identifying, on a library of 527 drugs, molecules able to negatively affect the expression of TRF2, a key protein in telomere maintenance. FK866, resulting from the screening as the best candidate hit, was then validated at biochemical and molecular levels and the mechanism underlying its activity in telomere deprotection was elucidated both in vitro and in vivo. The results of this study allow us to discover a novel role of FK866 in promoting, through the production of reactive oxygen species, telomere loss and deprotection, two events leading to an accumulation of DNA damage and tumor cell death. The ability of FK866 to induce telomere damage and apoptosis was also demonstrated in advanced preclinical models evidencing the antitumoral activity of FK866 in triple-negative breast cancer-a particularly aggressive breast cancer subtype still orphan of targeted therapies and characterized by high expression levels of both NAMPT and TRF2. Overall, our findings pave the way to the development of novel anticancer strategies to counteract triple-negative breast cancer, based on the use of telomere deprotecting agents, including NAMPT inhibitors, that would rapidly progress from bench to bedside.

PMID:37858982 | DOI:10.1111/acel.13944

J Biomed Inform. 2023 Oct 17:104528. doi: 10.1016/j.jbi.2023.104528. Online ahead of print.

ABSTRACT

MOTIVATION: Drug repurposing (DR) is an imminent approach to identifying novel therapeutic indications for the available drugs and discovering novel drugs for previously untreatable diseases. Nowadays, DR has major attention in the pharmaceutical industry due to the high cost and time of launching new drugs to the market through traditional drug development. DR task majorly depends on genetic information since the drugs revert the modified Gene Expression (GE) of diseases to normal. Many of the existing studies have not considered the genetic importance of predicting the potential candidates.

METHOD: We proposed a novel multimodal framework that utilizes genetic aspects of drugs and diseases such as genes, pathways, gene signatures, or expression to enhance the performance of DR using various data sources. Firstly, the heterogeneous biological network (HBN) is constructed with three types of nodes namely drug, disease, and gene, and 4 types of edges similarities (drug, gene, and disease), drug-gene, gene-disease, and drug-disease. Next, a modified graph auto-encoder (GAE*) model is applied to learn the representation of drug and disease nodes using the topological structure and edge information. Secondly, the HBN is enhanced with the information extracted from biomedical literature and ontology using a novel semi-supervised pattern embedding-based bootstrapping model and novel DR perspective representation learning respectively to improve the prediction performance. Finally, our proposed system uses a neural network model to generate the probability score of drug-disease pairs.

RESULTS: We demonstrate the efficiency of the proposed model on various datasets and achieved outstanding performance in 5-fold cross-validation (AUC=0.99, AUPR=0.98). Further, we validated the top-ranked potential candidates using pathway analysis and proved that the known and predicted candidates share common genes in the pathways.

PMID:37858852 | DOI:10.1016/j.jbi.2023.104528